3D LiDAR aided global navigation satellite system and the method for non-line-of-sight detection and correction

Abstract

A method for supporting positioning of a vehicle using a satellite positioning system is disclosed. The method includes generating, in real-time, a sliding window map (SWM) based on 3D point clouds from a 3D LiDAR sensor and an attitude and heading reference system (AHRS), wherein the SWM provides an environment description for detecting and correcting a non-line-of-sight (NLOS) reception; accumulating the 3D point clouds from previous frames into the SWM for enhancing a field of view (FOV) of the 3D LiDAR sensor; receiving global navigation satellite system (GNSS) measurements from satellites, by a GNSS receiver; detecting NLOS reception from the GNSS measurements using the SWM; correcting the NLOS reception by NLOS remodeling when a reflection point is not found in the SWM; and estimating a GNSS positioning by a least-squares algorithm. It is the objective to provide a method that mitigates NLOS caused by both static buildings and dynamic objects.

What is claimed is: 
     
       1. A method, used by a vehicle, for supporting positioning of the vehicle using a satellite positioning system, the method comprising:
 receiving a LiDAR factor and an inertial measurement unit (IMU) factor, from a 3D LiDAR sensor and a LiDAR inertial odometry (LIO); 
 integrating the LiDAR factor and the IMU factor using a local factor graph optimization for estimating a relative motion between two epochs; 
 generating a 3D point cloud map (PCM) as an auxiliary landmark satellite for providing a low elevation auxiliary landmark satellite; 
 receiving global navigation satellite system (GNSS) measurements from satellites, by a GNSS receiver; 
 detecting a GNSS non-line-of-sight (NLOS) reception from the GNSS measurements using the 3D PCM; and 
 excluding the GNSS NLOS reception from the GNSS measurements to obtain survived GNSS satellite measurements for enhancing GNSS measurement quality for the positioning of the autonomous driving vehicle. 
 
     
     
       2. The method of  claim 1  further comprising:
 performing a global navigation satellite system real-time kinematic (GNSS-RTK) float estimation on the survived GNSS satellite measurements to obtain a float solution; 
 performing an ambiguity resolution to obtain a fixed ambiguity solution; and 
 determining a fixed GNSS-RTK positioning solution from the float solution and the fixed ambiguity solution. 
 
     
     
       3. The method of  claim 2 , wherein the ambiguity resolution is performed by applying a LAMBDA algorithm. 
     
     
       4. The method of  claim 2  further comprising:
 feedbacking the fixed GNSS-RTK positioning solution to the 3D LiDAR sensor and the LIO; and 
 performing PCM correction using the fixed GNSS-RTK positioning solution for correcting a drift of 3D point clouds. 
 
     
     
       5. The method of  claim 2  further comprising obtaining an initial guess of the float solution using least-squares algorithms on the GNSS measurements. 
     
     
       6. A method, used by a vehicle, for supporting positioning of the vehicle using a satellite positioning system, the method comprising:
 generating, in real-time, a sliding window map (SWM) based on 3D point clouds from a 3D LiDAR sensor and an attitude and heading reference system (AHRS), wherein the SWM provides an environment description for detecting and correcting a non-line-of-sight (NLOS) reception; 
 accumulating the 3D point clouds from previous frames into the SWM for enhancing a field of view (FOV) of the 3D LiDAR sensor; 
 receiving, by a GNSS receiver, global navigation satellite system (GNSS) measurements from satellites; 
 detecting NLOS reception from the GNSS measurements using the SWM; 
 correcting the NLOS reception by NLOS remodeling when a reflection point is not found in the SWM; and 
 estimating a GNSS positioning by a least-squares algorithm. 
 
     
     
       7. The method of  claim 6  further comprising minimizing a magnitude of draft of the SWM by excluding point clouds far away from the GNSS receiver, such that the 3D point clouds are inside a sliding window. 
     
     
       8. The method of  claim 6 , wherein the step of generating the SWM comprises:
 obtaining a local map from a LiDAR scan-matching based on the 3D point clouds from the 3D LiDAR sensor; and 
 adopting an orientation from the AHRS to transform the SWM from a body frame to a local East North Up (ENU) frame. 
 
     
     
       9. The method of  claim 6 , wherein the step of detecting the NLOS reception from the GNSS measurements is performed using a fast-searching method, wherein the fast-searching method comprises:
 initializing a search point at a center of the 3D LiDAR sensor; 
 determining a search direction connecting the GNSS receiver and a satellite based on an elevation angle and an azimuth angle of the satellite; 
 moving the search point along the search direction with a fixed incremental value Δd pix ; 
 counting a number of neighboring points (N k ) near the search point; and 
 classifying the search point as an NLOS satellite if N k  exceeds a predetermined threshold N thres . 
 
     
     
       10. The method of  claim 6  further comprising correcting the NLOS reception by re-estimating the GNSS measurement using a model calibration based on the SWM, wherein the SWM provides the 3D point clouds that are dense, discrete, and unorganized without continuous building surfaces or boundaries. 
     
     
       11. The method of  claim 10 , wherein the model calibration comprises:
 detecting a reflection point corresponding to the NLOS reception using an efficient kdTree structure based on a reflector detection algorithm, wherein the reflector detection algorithm comprises:
 traversing all the azimuths from 0° to 360° with an azimuth resolution of α res  and an elevation angle of ϵ r,t   s ; 
 detecting a potential reflector when a line-of-sight connecting the point p j  and a satellite is not blocked; and 
 detecting a unique reflector with a shortest distance between the GNSS receiver and the potential reflectors. 
 
 
     
     
       12. The method of  claim 6 , wherein the NLOS remodeling is performed using a weighting scheme with a scaling factor, wherein the scaling factor is used to de-weight the NLOS reception, wherein the weighting scheme comprises a determination that:
 if a satellite is classified as a LOS measurement, the scaling factor is calculated based on a satellite signal to noise ratio (SNR) and an elevation angle; 
 if the satellite is classified as an NLOS measurement and a pseudorange error is corrected, the scaling factor is calculated based on the satellite SNR and the elevation angle; and 
 if the satellite is classified as an NLOS measurement but the reflection point is not detected, the scaling factor is calculated based on the satellite SNR, the elevation angle, and a scaling factor K w . 
 
     
     
       13. A LiDAR aided global navigation satellite system, used by a vehicle, for supporting positioning of the vehicle using a satellite positioning system, the system comprising:
 a 3D LiDAR sensor; 
 an attitude and heading reference system (AHRS); 
 a global navigation satellite system (GNSS) receiver configured to receive GNSS measurements from satellites; 
 a processor communicatively connected to the 3D LiDAR sensor, the AHRS, and the GNSS receiver, wherein the processor is configured to:
 generate, in real-time, a sliding window map (SWM) based on 3D point clouds from the 3D LiDAR sensor and the AHRS, wherein the SWM provides an environment description for detecting and correcting a non-line-of-sight (NLOS) reception; 
 accumulate the 3D point clouds from previous frames into the SWM for enhancing a field of view (FOV) of the 3D LiDAR sensor; 
 detect NLOS reception from the GNSS measurements from the GNSS receiver using the SWM; 
 correct the NLOS reception by NLOS remodeling when a reflection point is not found in the SWM; and 
 estimate a GNSS positioning by a least-squares algorithm. 
 
 
     
     
       14. The system of  claim 13 , wherein the processor is further configured to minimize a magnitude of draft of the SWM by excluding point clouds far away from the GNSS receiver, such that the 3D point clouds are inside a sliding window. 
     
     
       15. The system of  claim 13 , wherein the processor is configured to obtain a local map based on the 3D point clouds from the 3D LiDAR sensor; and adopts an orientation from the AHRS to transform the SWM from a body frame to a local East North Up (ENU) frame. 
     
     
       16. The system of  claim 13 , wherein the processor is configured to use a fast-searching method to detect the NLOS reception from the GNSS measurements, wherein the fast-searching method comprises:
 initializing a search point at a center of the 3D LiDAR sensor; 
 determining a search direction connecting the GNSS receiver and a satellite based on an elevation angle and an azimuth angle of the satellite; 
 moving the search point along the search direction with a fixed incremental value Δd pix ; 
 counting a number of neighboring points (N k ) near the search point; and 
 classifying the search point as an NLOS satellite if N k  exceeds a predetermined threshold N thres . 
 
     
     
       17. The system of  claim 13 , wherein the processor is further configured to correct the NLOS reception by re-estimating the GNSS measurement using a model calibration based on the SWM, wherein the SWM provides the 3D point clouds that are dense, discrete, and unorganized without continuous building surfaces or boundaries. 
     
     
       18. The system of  claim 17 , wherein the model calibration comprises:
 detecting a reflection point corresponding to the NLOS reception using an efficient kdTree structure based on a reflector detection algorithm, wherein the reflector detection algorithm comprises:
 traversing all the azimuths from 0° to 360° with an azimuth resolution of α res  and an elevation angle of ϵ r,t   s ; 
 detecting a potential reflector when a line-of-sight connecting the point p j  and a satellite is not blocked; and 
 detecting a unique reflector with a shortest distance between the GNSS receiver and the potential reflectors. 
 
 
     
     
       19. The system of  claim 13 , wherein the processor is configured to perform the NLOS remodeling using a weighting scheme with a scaling factor, wherein the scaling factor is used to de-weight the NLOS reception, wherein the weighting scheme comprises a determination that:
 if a satellite is classified as a LOS measurement, the scaling factor is calculated based on a satellite signal to noise ratio (SNR) and an elevation angle; 
 if the satellite is classified as an NLOS measurement and a pseudorange error is corrected, the scaling factor is calculated based on the satellite SNR and the elevation angle; and 
 if the satellite is classified as an NLOS measurement but the reflection point is not detected, the scaling factor is calculated based on the satellite SNR, the elevation angle, and a scaling factor K w . 
 
     
     
       20. The system of  claim 13  further comprising:
 an autonomous control for controlling the vehicle based on the GNSS positioning, thereby various functions for intelligent transportation systems are realized; 
 an user interface for activating or deactivating the autonomous control; and 
 a communication interface.